Journal ArticleDOI
Threshold heteroskedastic models
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TLDR
In this paper, the conditional standard deviation is a piecewise linear function of past values of the white noise, which allows different reactions of the volatility to different signs of the lagged errors.About:
This article is published in Journal of Economic Dynamics and Control.The article was published on 1994-09-01. It has received 2125 citations till now. The article focuses on the topics: Autoregressive conditional heteroskedasticity & Heteroscedasticity.read more
Citations
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Asymmetry and downside risk in foreign exchange markets
Shaun A. Bond,Stephen Satchell +1 more
TL;DR: In this article, the double gamma distribution is used as a means of modelling asymmetry in the conditional distribution of financial data and the results for the Malaysian Riggit, Zimbabwe Dollar and the Korean Won demonstrate the extreme downside volatility experienced by these countries during the emerging markets currency crisis.
Journal ArticleDOI
GJR-GARCH model in value-at-risk of financial holdings
TL;DR: In this article, an asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) model, Glosten, Jagannathan and Runkle-GARCH, was introduced to evaluate the market risk of financial holdings.
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Dynamic model for hedging of the European stock sector with credit default swaps and EURO STOXX 50 volatility index futures
TL;DR: In this paper, the time-varying correlations between CDS and portfolio design are estimated for the purpose of examining whether CDS can act as a hedge and safe haven for the European stock sectors.
Dissertation
Démarche analytique dans la construction des études d'évènement sur les marchés étroits : Application à la Bourse des Valeurs Mobilières de Tunis
TL;DR: In this paper, a demarche par simulations, effectuees sur des donnees reelles de la Bourse de Tunis, nous a permis de juger de la validite des differentes methodes a utiliser and des differents tests a mettre en œuvre, puis de determiner celles and ceux qui sont recommandes en fonction des caracteristiques de l’evenement a etudier.
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Modelling Volatility Dynamics of Cryptocurrencies Using GARCH Models
TL;DR: Modelling the volatility dynamics of eight most popular cryptocurrencies in terms of their market capitalization for the period starting from 7th August 2015 to 1st August 2018 shows that the asymmetric GARCH models with long memory property and heavy-tailed innovations distributions overall perform better for all cryptocurrencies.
References
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Journal ArticleDOI
Handbook of Mathematical Functions
Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Journal ArticleDOI
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
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Conditional heteroskedasticity in asset returns: a new approach
TL;DR: In this article, an exponential ARCH model is proposed to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987, which is an improvement over the widely-used GARCH model.
Journal ArticleDOI
On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks
TL;DR: In this article, a modified GARCH-M model was used to find a negative relation between conditional expected monthly return and conditional variance of monthly return, using seasonal patterns in volatility and nominal interest rates to predict conditional variance.